1 citations · 1 across the 11 of their papers we have counts for
13 papers
SPINET: Sheaf Protein Inverse Folding Network
Jens Lundsgaard, Colin Mikulski, Zhixuan Yan +1
Proteins change shape as they function, yet most inverse folding models predict amino acid sequences from a single, fixed backbone. A central challenge in protein engineering is to…
T-SNN: Temporal Simplicial Neural Network for EEG Decoding
Nikita Malik, Shubhajit Roy, Mohit Kataria +4
Decoding brain states requires models that capture both the evolution of neural activity and interactions among groups of brain regions. Existing EEG methods often treat recordings…
BrainDyn: A Sheaf Neural ODE for Forecasting and Generating Brain Dynamics
Siddharth Viswanath, Panayiotis Ketonis, Chen Liu +11
Brain activity unfolds as coordinated patterns across regions over time, and modeling these dynamics is a central goal in computational neuroscience. Because activity is distribute…
scShapeBench: Discovering geometry from high dimensional scRNAseq data
Andrew J Steindl, João Felipe Rocha, Brian Tshilengi Di Bassinga +13
High-dimensional point cloud data arise across many scientific domains, especially single-cell biology. The shapes or topologies of these datasets determine the types of informatio…
MIOFlow 2.0: A unified framework for inferring cellular stochastic dynamics from single cell and spatial transcriptomics data
Xingzhi Sun, João Felipe Rocha, Brett Phelan +11
Understanding cellular trajectories via time-resolved single-cell transcriptomics is vital for studying development, regeneration, and disease. A key challenge is inferring continu…
Linearized Optimal Transport for Analysis of High-Dimensional Point-Cloud and Single-Cell Data
Tianxiang Wang, Yingtong Ke, Dhananjay Bhaskar +2
Single-cell technologies generate high-dimensional point clouds of cells, enabling detailed characterization of complex patient states and treatment responses. Yet each patient is…
STAGED: A Multi-Agent Neural Network for Learning Cellular Interaction Dynamics
Joao F. Rocha, Ke Xu, Xingzhi Sun +6
The advent of single-cell technology has significantly improved our understanding of cellular states and subpopulations in various tissues under normal and diseased conditions by e…
Principal Curvatures Estimation with Applications to Single Cell Data
Yanlei Zhang, Lydia Mezrag, Xingzhi Sun +6
The rapidly growing field of single-cell transcriptomic sequencing (scRNAseq) presents challenges for data analysis due to its massive datasets. A common method in manifold learnin…
HiPoNet: A Multi-View Simplicial Complex Network for High Dimensional Point-Cloud and Single-Cell Data
Siddharth Viswanath, Hiren Madhu, Dhananjay Bhaskar +7
In this paper, we propose HiPoNet, an end-to-end differentiable neural network for regression, classification, and representation learning on high-dimensional point clouds. Our wor…
Exploring the Manifold of Neural Networks Using Diffusion Geometry
Elliott Abel, Andrew J. Steindl, Selma Mazioud +12
Drawing motivation from the manifold hypothesis, which posits that most high-dimensional data lies on or near low-dimensional manifolds, we apply manifold learning to the space of…
ProtScape: A molecular structure and energy-aware representation for protein conformation generation
Siddharth Viswanath, Dhananjay Bhaskar, Xingzhi Sun +13
Molecular dynamics (MD) simulations are a principled but computationally expensive approach for studying protein conformational variability, making it challenging to generate large…
Looking through the mind's eye via multimodal encoder-decoder networks
Arman Afrasiyabi, Erica Busch, Rahul Singh +4
In this work, we explore the decoding of mental imagery from subjects using their fMRI measurements. In order to achieve this decoding, we first created a mapping between a subject…
Latent Representation Learning for Multimodal Brain Activity Translation
Arman Afrasiyabi, Dhananjay Bhaskar, Erica L. Busch +5
Neuroscience employs diverse neuroimaging techniques, each offering distinct insights into brain activity, from electrophysiological recordings such as EEG, which have high tempora…